biodata-registry

A pip-installable Python package that provides a YAML-based dataset manifest registry for bioinformatics agents. It also exposes a FastMCP server so agent processes can query dataset metadata over MCP.

What it does

• Discovers and indexes dataset manifests (one YAML file per dataset) at import time
• Validates manifests against a typed schema (DatasetManifest)
• Ships with the Moffitt et al. 2015 PDAC bulk microarray dataset as the first entry
• Exposes registry tools over MCP via a FastMCP server

Installation

pip install -e /path/to/biodata-registry

Or from within the repo directory:

pip install -e .

Python usage

from biodata_registry import get_registry, load_manifest, list_available_datasets

# List all registered datasets
datasets = list_available_datasets()
# [{'dataset_id': 'gse71729_moffitt', 'title': '...', 'modality': 'bulk_microarray', ...}]

# Get a typed manifest
manifest = load_manifest("gse71729_moffitt")
print(manifest.organism)          # 'human'
print(manifest.analysis_path)     # 'B'  (log_expression → Path B)
print(manifest.default_contrasts) # list of contrast dicts

# Raw registry access
reg = get_registry()
raw = reg.get("gse71729_moffitt")  # dict or None
print(reg.list())                   # ['gse71729_moffitt']

Running the MCP server

python -m biodata_registry.server

The server exposes four MCP tools:

Tool	Description
list_datasets	List all dataset IDs with title and modality
get_manifest	Return the full manifest dict for a dataset
get_prohibited_inferences	Return refusal rules from a manifest
get_contrast_definition	Return contrast metadata for a specific column

Adding a new dataset

1. Create a YAML file in biodata_registry/manifests/<dataset_id>.yaml.
2. The file must contain at minimum: dataset_id, title, accession, organism, modality, platform, data_level, feature_id_type, expression_source, metadata_source, group_columns, valid_workflows, limitations.
3. The registry auto-discovers it on next import — no code changes needed.
4. Run python -m pytest tests/ to verify the new manifest loads correctly.

See biodata_registry/manifests/gse71729_moffitt.yaml for a fully annotated example.

Schema reference

Manifest fields are documented in biodata_registry/manifest_schema.py. Key controlled vocabularies:

• organism: human, mouse
• modality: bulk_microarray, bulk_rnaseq, sc_rnaseq, spatial_rnaseq, proteomics
• data_level: raw_counts, log_expression, log_ratio, normalized, tpm, fpkm, protein_abundance
• feature_id_type: probe_id, gene_symbol, ensembl_gene_id, entrez_id, protein_id

Security-review orientation

This package is the system's trust anchor: the consuming agents validate every user-named dataset_id against this registry and then trust the manifest's expression_source URL. So the security-relevant surface is data integrity, not runtime attack surface:

• Manifest schema (biodata_registry/manifest_schema.py) — the typed DatasetManifest and its controlled vocabularies; validate() (decomposed into _check_* helpers) is what rejects malformed manifests at import time.
• Integration gates (biodata_registry/integration.py) — get_integration_plan decides early / late / concordance / refuse and enforces the confound, same-cohort, and cross-resolution safety gates. This is pure metadata logic.
• No secrets, no network, no code execution at import — the registry only reads bundled YAML and returns dictionaries. It runs no user input and holds no credentials. The expression-h5ad URLs it hands out point at a private HF dataset; the consumers authenticate to fetch them.
• Distribution: consumers pin a specific commit via a git+https://…@<commit> wheel URL, so the exact manifest set under review is pinned and auditable.